Traffic Optimization
Our project aims to improve traffic management efficiency with a Modular Ruleset Traffic Algorithm and Camera Vision to perceive intersection environments.
Traffic Optimization GitHub
Traffic Optimization GitHub
4 Model Comparison at Evansdale
4 Model Comparison at Evansdale
YOLO11n Detection Images
YOLO11n Detection Images
RT-DETRv2-S Detection Images
RT-DETRv2-S Detection Images
CV Module Training Log: Training Ultralytics YOLO11n
CV Module Training Log: Training Ultralytics YOLO11n
Jerin Adkins West Virginia University Camera Vision Module CV Module Training Log: Training Ultralytics YOLO11n [Iteration 1] Domain Robustness Benchmark Date: 2/25/26 Goal: Establish a high-accuracy daytime baseline and evaluate model performance against external (non-site-specific) traffic en...
How YOLO11n and RT-DETRv2-S work
How YOLO11n and RT-DETRv2-S work
How the YOLO11n model works: The CNN (Convolutional Neural Network) (the neural network layers) look at the pixels and produces a massive list of thousands of potential boxes, regardless of instances within the frames. The NMS (Non-Maximum Suppression) is a mathematical algorithm within the model...
Understanding mAP Benchmarking
Understanding mAP Benchmarking
Understanding mAP Benchmarking To evaluate the precision of the CV Module, two variants of Mean-Average Precision (mAP) are utilized. These metrics differ based on the Intersection over Union (IoU) threshold – the percentage of overlap required between the model’s predicted “box” and the actual “...
Edge Deployment Verdict
Edge Deployment Verdict
Edge Deployment Verdict: YOLO11n Based on the comparative analysis of computation complexity, thermal reliability, and real-time safety requirements, YOLO11n is the recommended architecture for roadside edge deployment. While RT-DETRv2-S provides deterministic latency and high precision, its arch...
Latency and Power Analysis for YOLO11n and RT-DETRv2-S
Latency and Power Analysis for YOLO11n and RT-DETRv2-S
What is Latency for a YOLO model? Latency is the End-to-End Inference time for a single image. This time measurement is composed of 3 stages: Pre-processing: Resizing the camera feed down to 640 x 640 pixels that the model is expecting and normalizing the color values. Inference (The CNN): This i...
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